Senior/Staff Software Engineer, Motion Planning

Responsibilities

Design and implement algorithms for decision-making and trajectory generation, ensuring safety, comfort, and reliability across a wide range of driving conditions.
Bring novel robotics and machine learning techniques from research into a production-grade, safety-critical system.
Reason about complex, socially-nuanced driving situations, such as negotiating merges, junctions, and interactions with other road users, under uncertainty.
Utilize modern C++ numerical optimization frameworks (e.g., CasADi, Acados) and industry-standard tooling (git, ROS, etc.) to integrate new planning algorithms into the production stack.
Partner closely with the ML and prediction teams to integrate, refine, and act on probabilistic and multi-future representations of how a scenario might evolve, shaping how these outputs are consumed by planning decisions.
Perform system-level diagnostics and tuning using in-house and open-source visualization tools (e.g., webviz).
Collaborate cross-functionally and communicate clearly, producing system designs that are reviewed and approved at a high level before implementation begins.

Required skills

MS with 1-2 years of relevant experience, or PhD, in Computer Science, Robotics, or a related field.
Strong production software development skills in C++; Python experience a plus.
Robotics expertise in one or more of: path planning, motion planning, trajectory generation, behavior planning, optimal control.
Strong math (geometry, analysis, probability) and physics (kinematics, dynamics) fundamentals.
Experience designing, implementing, and refining planning algorithms on a real-world robotic or vehicle platform.
Strong oral and written communication skills, with the ability to collaborate across teams and derive solutions from cross-functional input

Preferred skills

PhD + 1-2 years or MS + 3-4 years of relevant experience.
Bonus: exposure to machine learning techniques such as deep learning, imitation learning, transformer-based models, or diffusion-based models, particularly as applied to decision-making or scenario generation.
Familiarity with safety frameworks for autonomy (e.g., RSS, collision avoidance/MRM logic).
Prior experience working on safety-critical systems.